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Neural nets supplant marker genes in analyzing single cell RNA sequencing

19:00 EST 12 Nov 2018 | AAAS

(Carnegie Mellon University) Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding, published in the online journal Nature Communications, could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.

Original Article: Neural nets supplant marker genes in analyzing single cell RNA sequencing

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